This layer implements the operation:
outputs = activation(inputs * kernel + bias)
Where activation is the activation function passed as the activation
argument (if not None), kernel is a weights matrix created by the layer,
and bias is a bias vector created by the layer
(only if use_bias is True).
Arguments
units
Integer or Long, dimensionality of the output space.
activation
Activation function (callable). Set it to None to maintain a
linear activation.
use_bias
Boolean, whether the layer uses a bias.
kernel_initializer
Initializer function for the weight matrix.
If None (default), weights are initialized using the default
initializer used by tf.compat.v1.get_variable.
bias_initializer
Initializer function for the bias.
kernel_regularizer
Regularizer function for the weight matrix.
bias_regularizer
Regularizer function for the bias.
activity_regularizer
Regularizer function for the output.
kernel_constraint
An optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
bias_constraint
An optional projection function to be applied to the
bias after being updated by an Optimizer.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]